The amount of data that is getting generated on a day-to-day basis is huge. That is why companies across the globe are turning data into information and are using it to optimise their strategies. But the challenge here is the fact that every company needs a professional with relevant skills to extract insights from the massive data collected — a data scientist who’s now getting a seat at the big table.
Further, with the evolution of data and its increasing use in different types of business, people have started to see data science as an uber-cool job. However, when it comes to becoming a data scientist, we notice a lot of professionals have dozens of MOOC courses and fancy buzzwords on their resumes or LinkedIn profiles. And when a data science neophyte sees these portfolios, they get the impression that data science is not their cup of tea. However, that is not the case all the time — data science is about solving an actual business problem, making the best out of the cluttered data. If you have the relevant knowledge, you can kickstart your data science career without any prior experience.
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Steps To Follow
There are many aspirants who want to be a part of the data science community, but they are clueless about how to get started, and there could be several reasons behind it — maybe they didn’t have a data science subject in their formal education, maybe they never attended any data science conference, maybe there are not many faculties who are much aware of the domain, etc.
In this article, we are going to outline some of the important factors to bear in mind and prepare for a data science job without any prior experience.
This the first and foremost thing to do when you are starting your data science journey and you don’t have any prior experience. Ask yourself these questions: why would a company would hire you? If they are not hiring you, what could be the reason? What do you know about the data science domain? What more do you need to know about the domain? What extra skills do you need to learn to stand out from the crowd?
Further, along with the skills and knowledge a data science professionals should have, learn about the latest industry trends — how corporate works, what are the current job roles that are on demand, what are the latest programming languages etc. Make a list of all the things you know, and you need to know and make a plan for how you should go about them.
2. Skills You Need To Master
Mathematics: It is also considered as one of the vital elements when it comes to data science. It is very important in the field of data science as there are many concepts that help a data scientist with algorithms. Also, concepts like statistics and probability theory are key for algorithms implementation. So, make sure you put in a lot of effort into sharpening your mathematical skills.
Programming: There are many people who would suggest a huge bunch of programming languages to learn if you want to have a career in data science. However, don’t overwhelm yourself with all the hype talks. When it comes to data science Python and R are the two most important programming languages. Put in your complete focus on these two languages at the initial stage. Later, when you gain confidence along with significant confidence, you can move on to the next one (Java could be one of them).
To learn to programme you can always take up short term course or online courses. Also, practice a lot. The more you code, the better coder you become.
Communication & Visualisation: Having an upper hand on all the technicalities is one but to be a successful data scientist you also need to have outstanding communication and presentation skills. You should not just be a data scientist but be a data storyteller too. Why? Once you get the valuable insights from the cluttered data, your next job is to present it, and if you don’t have storytelling skills, how would make others understand what the insights are capable of and the value they would deliver.
3. Practice With Real-Time Problem Statement
Learning and mastering skills are definitely mandatory, but to make the most out of your learning, you need to practice — practice with real-time problem statements you give your data science learning a worth. The more you solve those problems the more you gain experience as well as confidence and makes the pathway to your dream science job short. There are many hackathons available on the internet — you can always pick one, participate and see where you stand in this ever competitive data science domain.
4.Connect With Leaders
It is always considered to be a good practice to take advice from someone who has already mastered domain. And for that, you can make the best use of platforms like LinkedIn to connect with some of the leaders from the industry.
Another best ways to make connections is by attending data science conferences, where you not only get to attend talks and masterclasses but also meet a lot of people from the industry who would help you take a right path when you are starting with your data science journey.
5. Accept Reality
It is no surprise that data science is one of the highest paying and reputed jobs right now in the industry. And no company would pay someone a handsome paycheck and give a high-level designation until and unless they prove that s/he is capable of dealing with and some of the complex business problems. So, accept the fact that when you initially start your career, you might not even get the designation as a data scientist (you might get in some exceptional cases). However, if you are determined and learn more and more about the domain, the chance of you getting to a higher position with a significantly high paycheck increases.
Make sure you don’t hesitate to seek help from fellow data scientist when you need. Knowledge and skills are the master keys to success.